Automatic extraction of building roof contours by laser scanning data and markov random field

Abstract

This paper proposes a methodology for automatic extraction of building roof
contours from a Digital Elevation Model (DEM), which is generated through the
regularization of an available laser point cloud. The methodology is based on two
steps. First, in order to detect high objects (buildings, trees etc.), the DEM is
segmented through a recursive splitting technique and a Bayesian merging
technique. The recursive splitting technique uses the quadtree structure for
subdividing the DEM into homogeneous regions. In order to minimize the
fragmentation, which is commonly observed in the results of the recursive splitting
segmentation, a region merging technique based on the Bayesian framework is
applied to the previously segmented data. The high object polygons are extracted by
using vectorization and polygonization techniques. Second, the building roof
contours are identified among all high objects extracted previously. Taking into
account some roof properties and some feature measurements (e. g., area,
rectangularity, and angles between principal axes of the roofs), an energy function
was developed based on the Markov Random Field (MRF) model. The solution of
this function is a polygon set corresponding to building roof contours and is found
by using a minimization technique, like the Simulated Annealing (SA) algorithm. Experiments carried out with laser scanning DEM´s showed that the methodology
works properly, as it delivered roof contours with approximately 90% shape
accuracy and no false positive was verified.